Applications of Geomechanics to Hydraulic Fracturing: Case Studies From Coal Stimulations
Bibliographic record
Abstract
Summary Modern hydraulic-fracture treatments are designed by use of fracture simulators that require formation-related inputs, such as in-situ stresses and rock mechanical properties, to optimize stimulation designs for targeted reservoir zones. Log-derived stress and mechanical properties that are properly calibrated with injection data provide critical descriptions of variations in different lithologies at varying depths. From a practical standpoint, however, most of the hydraulic-fracturing simulators that are currently used for treatment design use only a limited portion of a geologic-based rock-mechanical-property characterization, thus resulting in outputs that may not completely align with observed outcomes from a fracturing treatment. By use of examples from hydraulic-fracture stimulations of coals in a complex but well-characterized stress environment in Surat Basin of eastern Australia, we obtain the reservoir-rock-related input parameters that are important for the design of hydraulic fractures and also identify those that are not essential. To understand the effect on improving future fracture-stimulation designs, the authors present work flows for pressure-history matching of treatments and subsequent comparison of corresponding geometries with external measurements, such as microseismic (MS) surveys, to calibrate geomechanical models. The paper presents examples discussing synergies, discrepancies, and gaps that currently exist between “geologic” geomechanical concepts in contrast to the geomechanical descriptions and concepts that are used and implemented in hydraulic-fracturing stimulations. Ultimately it remains paramount to constrain as many critical variables as realistically and as uniquely as possible. Significant emphasis is placed on reservoir-specific pretreatment data acquisition and post-treatment analysis. Some of the obvious differences observed between the measured and fracture-model-derived geometries are also presented in the paper, highlighting the areas in fracture modeling where significant improvement is needed. The approach presented in this paper can be used to refine hydraulic-fracture-treatment designs in similar complex reservoirs worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".